With the advent of big data era, tensor expression of data has been widely used. However, during achieving the invention, the inventor found out that in prior art vector model algorithm is still utilized to process tensor data. On the basis of the concept of vector model algorithm, during a preprocessing phase, feature extraction for original data (vectorization) should be performed, which firstly is easy to destroy spatial information and inner correlation which are specific to tensor data, secondly possesses superabundant modern parameters which would easily lead to issues such as curse of dimensionality, over learning and small amount of samples.
A plurality of tensor mode algorithms have become a trend of the era. However, solving an objective function of STM is a non-convex optimization issue, in which solving by using an alternative projection method is required; the time complexity of the algorithm is high and a local minimum value occurs frequently.